I am posting this here because I think it is relevant to discussion
on this topic. My goal isn't to prove or disprove anything related to
true origins of Japanese, and I will identify several flaws in the
fundamentals that would rule this info out as hard evidence of one
origin vs. another. However, I think the results are interesting in the
context of discussion of Japanese origins on this thread.
I
am of Japanese and mixed European maternal ancestry, born in the USA.
My maternal grandmother is from Osaka. My maternal grandfather, from
what I can tell through traditional genealogy, is primarily of north
European ancestry, with most ancestors arriving from Germany, Prussia,
Norway, and England in the 1800's. My mtDNA is Haplogroup D, which is
common among multiple east Asian nationalities. I do not have a D
sub-group identified at this time.
I am of
mixed European paternal ancestry, with a few traditional genealogical
references to what may have been full or mixed Cherokee lineage. I have
no hard evidence (DNA, pictures, etc.) for Native American ancestry.
My Y-DNA is Haplogroup G and arrived in North America in the 1600's
from England. It is in minority there among R1a,R1b,etc. and exactly
how it arrived in England is unknown. G is more common among
populations in the Mediterranean and Caucasus regions, and also exists
in some fairly large numbers in Central Asia among the Magyars (likely
ancestors of Huns) and other groups like Uighurs. It also exists in
Afghanistan, Pakistan, and to a lesser degree India. All of this is
background on my paternal lineage, not relevant to origins of Japanese,
but relevant to the autosomal DNA analysis results below, which should
be based on both paternal and maternal genetic matches.
The
provider for the autosomal DNA results below is a private company DNA
Tribes. I have some skepticism about the accuracy of their methods.
Their autosomal matching algorithms and database are proprietary. The
science of autosomal matching for deep ancestry is criticized as inaccurate. However, I can say that I have a completely anglo-American
name, and answered no questionnaire to indicate I had any Asian
heritage, so all Asian matches they have provided were not biased by
outside information.
The aspect of these
results that surprised me the most were the number and strength of
Indian and Australian Aboriginal matches that I had. I fully expected
to have many European matches, as well as a number of east and central
Asian matches. The other thing that surprised me was that despite
Japanese being my single most identifiable ethnicity at 25%, there were
very few hits on Japanese groups, and those hits were weak, none in the
top 100. This may be due to a simple lack of comprehensive Japanese
reference samples in the DNA Tribes database.
See
the list below. The label is obviously the nationality or ethnicity
being compared to, the (0.nn) is the percentage of match compared to the
entire reference population for that group, and the nnn.nn is a
multiplier representing the number of times more likely I am to be that
nationality, compared to a reference population for the entire world
population. I have included only my top 100 matches.
1 Salar (Qinghai, China) (0.62) 370.84 2 Kirgiz (Xinjiang, Chinese Turkestan) (0.3) 208.96 3 Oman (0.39) 206.58 4 Indian (Singapore) (0.51) 197.29 5 Turkey (0.28) 190.69 6 Evenki (Inner Mongolia, China) (0.34) 183.25 7 Bonan (Gansu, China) (0.4) 175.48 8 Indian (United Arab Emirates) (0.45) 174.53 9 Lazio, Italy (0.2) 162.10 10 Kamma Chaudhary (Andhra Pradesh, India) (0.43) 153.21 11 Uzbek (Xinjiang, Chinese Turkestan) (0.37) 143.12 12 Israel (0.22) 142.61 13 Tomsk, Russia (0.26) 135.26 14 South Asian (United Kingdom) (0.33) 132.52 15 European-Aboriginal (mixed) (South Australia) (0.31) 125.98 16 Costa Rica (0.23) 122.53 17 Spain (0.14) 121.39 18 Italy (0.2) 112.89 19 East Indian (Canada) (0.25) 111.88 20 Han (Xian, Shaanxi, China) (0.16) 110.41 21 Han (Henan, China) (0.15) 107.49 22 Han (Qinghai, China) (0.19) 104.64 23 Indian (Dubai, UAE) (0.4) 102.81 24 Mestizo (Argentina) (0.16) 100.55 25 European-Aboriginal (mixed) (Riverine Region, Australia) (0.23) 99.30 26 Southeast Asian (New Zealand) (0.36) 97.12 27 Kuwait (0.11) 94.29 28 Puerto Rican (Springfield, Massachusetts, U.S.A.) (0.17) 92.06 29 Greece (0.14) 91.84 30 Turkey (0.15) 90.14 31 European-Aboriginal (mixed) (Western Australia) (0.25) 89.98 32 Sergipe, Brazil (0.14) 88.96 33 Calabria, Italy (0.16) 87.31 34 European-Aboriginal (mixed) (Queensland, Australia) (0.32) 86.45 35 Italy (0.11) 83.20 36 Kurdish (Northern Iraq) (0.15) 82.77 37 Tu (Qinghai China) (0.4) 81.87 38 European-Aboriginal (mixed) (Northeast Australia) (0.55) 80.73 39 Istanbul, Turkey (0.16) 80.43 40 Oman (0.28) 79.74 41 Caucasian (Tasmania, Australia) (0.12) 78.48 42 Schleswig-Holstein, Germany (0.08) 78.30 43 Beijing, China (0.13) 78.10 44 Pakistan (0.29) 74.03 45 Spain (0.08) 71.68 46 Caucasian (New South Wales, Australia) (0.12) 70.32 47 Turkey (0.16) 70.07 48 European-Aboriginal (mixed) (New South Wales, Australia) (0.16) 69.92 49 Hungary (0.11) 69.78 50 Han (Shaanxi, China) (0.15) 69.04 51 Flemish (Belgium) (0.09) 68.93 52 Arab (Israel) (0.12) 66.91 53 Nordrhein-Westfalen, Germany (0.09) 65.91 54 Afghanistan (0.25) 65.74 55 Basque (Basque Country, Spain) (0.08) 65.44 56 Turkey (0.15) 64.96 57 European-Aboriginal (mixed) (Northern Territory, Australia) (0.37) 64.70 58 Caucasian (Capital Territory, Australia) (0.11) 64.66 59 Northwest Spain (0.09) 64.56 60 Bedouin (Negev, Israel) (0.18) 64.23 61 Northern Greece (0.11) 63.90 62 Flemish (0.1) 63.87 63 Hungary (0.11) 63.75 64 Genoa, Italy (0.19) 62.66 65 Dongxiang (Qinghai, China) (0.26) 62.61 66 Central and Southern Iraq (0.14) 62.29 67 Aboriginal (Tiwi Islands, Australia) (0.21) 61.84 68 Xibe (Xinjiang, Chinese Turkestan) (0.15) 60.99 69 Tu (Northwest China) (0.24) 60.24 70 Nepal (0.25) 60.09 71 Buddhist (Ladakh, India) (0.34) 57.56 72 Austria (0.08) 57.18 73 Brac, Croatia (0.1) 57.07 74 Csango (Romania) (0.06) 57.04 75 Turkey (0.13) 56.67 76 Gujarat, India (0.31) 56.49 77 Bogota, Colombia (0.17) 56.28 78 Greece (0.1) 56.24 79 Abov-Gemer, Eastern Slovakia (0.06) 55.70 80 Northern Portugal (0.06) 55.67 81 Han (North China) (0.1) 55.14 82 United Kingdom (0.08) 55.11 83 Greece (0.1) 54.83 84 Caucasian (U.S.A.) (0.09) 54.80 85 Toulouse, France (0.07) 54.73 86 Indian (Malaysia) (0.26) 53.97 87 Santa Fe, Argentina (0.15) 53.69 88 United Kingdom (0.1) 52.80 89 Han (Beijing, China) (0.08) 51.45 90 Buenos Aires, Argentina (0.13) 50.94 91 Central Portugal (0.07) 50.50 92 Northern Portugal (0.07) 50.36 93 London, England (0.09) 50.32 94 Greece (0.14) 49.41 95 Mainland Croatia (0.09) 49.41 96 Serbia (0.08) 49.41 97 Northern Pakistan (0.22) 49.32 98 Iban (Sarawak, Malaysia) (0.07) 48.66 99 Belem, Brazil (0.14) 48.62 100 Mendoza, Argentina (0.14) 47.97
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